The increasing popularity of mobile communications has placed a tremendous demand on the scarce radio resources of cellular communication networks. To efficiently utilize these valuable resources, radio frequencies in Time Division Multiple Access (TDMA) cellular systems such as GSM/EDGE, are being reused with closer proximity than ever. As a result, mutual interference among users occupying the same radio channel has become a major source of signal disturbance. The ability to suppress co-channel interference has become increasingly important for mobile receivers in cellular systems with tight reuse.
Multi-branch diversity or array processing is a class of commonly used techniques for suppressing interference, in which multiple versions of the same transmitted signal are produced and processed jointly in the receiver in order to cancel one or more interfering signal(s). The different signal versions may be obtained by using multiple receiving antennas, by sampling the received signal over the baud rate of transmission (i.e., oversampling), by separating in-phase (I) and quadrature-phase (Q) of the signal, or by combinations of these. The method of separating in-phase (I) and quadrature-phase (Q) of the signal is commonly referred to as the single-antenna-interference cancellation (SAIC) method and has recently received much attention in GERAN standardization.
In conventional array processing, the interference is typically modeled as temporally (across time) and/or spatially (across different signal versions) colored noise. By performing proper spatial and/or temporal noise whitening, the interference can be suppressed substantially. Such whitening operation may be performed before or during demodulation/equalization.
In order to suppress the noise or interference through spatial-temporal whitening, the receiver typically requires an estimate of a certain spectral property of the noise, such as the noise covariance matrix. From such spectral property, a whitening filter can then be derived to whiten, and therefore suppress, the noise. If the statistics of interference can be assumed to be approximately stationary over the data burst, which is the case in a nearly-synchronized network, the estimation of the noise spectral property may be performed over a sequence of training symbols in each data burst that is known to the receiver.
In addition, the demodulator or equalizer of the receiver must also be able to synchronize to the beginning of a data burst in order to begin demodulation. The synchronization process is typically done jointly with channel estimation over the training sequence. When spatial/temporal whitening is performed on the received signal to suppress noise or interference, the operating carrier-to-interference power ratio (C/I) can be changed so drastically that the ordinary method of synchronization and channel estimation, such as the least-squares (LS) method, can no longer produce an accurate synchronization position. As a result, the reliability of synchronization and channel estimation becomes a bottleneck of the overall receiver performance.
One known way of improving synchronization and quality of channel estimation in a multi-branch receiver is to first perform a certain initial synchronization and channel estimation, such as the LS channel estimation, and then estimate the noise covariance matrix or function based on the residual signal after channel estimation. From the estimated noise covariance matrix, a whitening filter can be computed using the well-known Whittle-Wiggins-Robinson Algorithm (WWRA) (or sometimes referred to as the generalized Levinson-Durbin algorithm). The problem with this approach is that the initial synchronization and channel estimation (before whitening) may not produce an accurate estimate of the synchronization position and the channel estimate. As a result, the statistics of the residual signal obtained from initial synchronization and channel estimation may not be representative of the statistics of the actual noise or interference.
Alternatively, the synchronization position and the coefficients of the whitening filter may be jointly estimated using the known indirect generalized least-squares (iGLS) algorithm. A by-product of this joint estimation is an estimate of the whitened channel response. However, the conventional iGLS algorithm does not allow efficient sharing of intermediate results produced at adjacent hypothesized synchronization positions. If the iGLS algorithm is brute-force applied at all synchronization positions, the computational requirements are greatly increased. In addition, the performance of the iGLS algorithm suffers when there are multiple interferers.